The 4 Biggest Problems with Big Data

People are buzzing about the promise of big data. That’s because it enables companies to obtain meaningful insights into customer behaviors, attitudes and preferences from their comments in social channels as well as through the other vast amounts of data that are generated through customer transactions and channel interactions.

Still, the amount of data and inputs that companies can draw upon is mind-boggling. And many business leaders need help determining which data sets to draw upon to address and, in some cases, identify pressing business issues that need resolution (e.g. customer churn, market share loss, etc.).

Indeed, analytics can help make sense of big data, in part, by helping companies identify the types of data and data sets that they should be examining to address specific business challenges. Still, there are significant challenges companies must overcome in order to exploit big data. Here are four of the most prominent:

A comprehensive approach to using big data. Most companies collect gobs of data but they don’t have comprehensive approaches for centralizing the information. According to a recent survey by LogLogic, 59% of the more than 200 security officers who responded say they are either using disparate systems for gathering data, not managing log data, or they use antiquated spreadsheets. The right analytics tools can definitely help to streamline and make sense of all this data, but a well-conceived strategy for collating data sources from different silos is still necessary.

Getting the right information into the hands of decision makers. Companies should use analytics “to avoid getting buried under the humongous amount of information they generate through various outlets,” according to a recent ZDNet Asia interview with XMG analyst Jacky Garrido. It’s true – too many companies lack coherent approaches to utilizing the gushers of customer and business data that are flowing into their organizations. As Garrido notes, as data is gathered, it needs to be mapped out. Moreover, critical data needs to be separated from insignificant or unnecessary data (e.g. inconsequential comments made by customers on Facebook or Twitter).

Effective ways of turning “big data” into “big insights.” No matter how you slice it, data is just that – data. In and of itself, data doesn’t necessarily provide decision makers with the kind of insights they need to do their jobs effectively or to take the next best actions based on discoveries about customer trends or other revelations about market conditions. This is where the right analytics tools are needed to help data scientists and business leaders make sense of the volumes of data that are pouring into their organizations. This includes the use of data visualization tools that can be used to help put data into context.

Big data skills are in short supply. There’s already a shortage of data scientists in the market. This includes a scarcity of people who know how to work well with large volumes of data and big data sets. Companies need the right mix of people to help make sense of the data streams that are coming into their organizations. This includes skills for applying predictive analytics to big data, a skill set that even most data scientists lack.